Deep learning model targeting cancer surrounding tissues for accurate cancer diagnosis based on histopathological images

作者：Lanlan Li, Yi Geng, Tao Chen, Kaixin Lin, Cheng-Jie Xie, Jing Qi, Hongan Wei, Jianping Wang, Dabiao Wang, Ze Yuan, Zixiao Wan, Tuoyang Li, Yanxin Luo, Decao Niu, Juan Li, Huichuan Yu · 发表于：Journal of Translational Medicine · 年份：2025 · DOI：10.1186/s12967-024-06017-6 · 被引用次数：7 · 研究领域：AI in cancer detection、Radiomics and Machine Learning in Medical Imaging、Colorectal Cancer Screening and Detection

Accurate and fast histological diagnosis of cancers is crucial for successful treatment. The deep learning-based approaches have assisted pathologists in efficient cancer diagnosis. The remodeled microenvironment and field cancerization may enable the cancer-specific features in the image of non-cancer regions surrounding cancer, which may provide additional information not available in the cancer region to improve cancer diagnosis. Here, we proposed a deep learning framework with fine-tuning target proportion towards cancer surrounding tissues in histological images for gastric cancer diagnosis. Through employing six deep learning-based models targeting region-of-interest (ROI) with different proportions of no-cancer and cancer regions, we uncovered the diagnostic value of non-cancer ROI, and the model performance for cancer diagnosis depended on the proportion. Then, we constructed a model based on MobileNetV2 with the optimized weights targeting non-cancer and cancer ROI to diagnose gastric cancer (DeepNCCNet). In the external validation, the optimized DeepNCCNet demonstrated excellent generalization abilities with an accuracy of 93.96%. In conclusion, we discovered a non-cancer ROI weight-dependent model performance, indicating the diagnostic value of non-cancer regions with potential remodeled microenvironment and field cancerization, which provides a promising image resource for cancer diagnosis. The DeepNCCNet could be readily applied to clinical diagnosis for gastric ...